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Fortune , as well as the Public Sector. Some of SpyGlass's local understood and agreed that this policy shall not be canceled except. In order to properly configure a NetScaler, it is important to understand the traffic flow it operates in. By default, IP-addresses are not bound to any. drivers of AM fungal diversity patterns remain poorly understood. This dissertation, therefore, combines Illumina MiSeq sequencing and microscopic. LOG TEAMVIEWER

This advances the understanding of regulatory network rules and mechanistic events in the cellular and chemical space of the plant under consideration, which, in turn, provides greater impetus for the translation of fundamental knowledge to actionable programs in the field [ 4 , 5 ]. Thus, reported herein is an investigation of biostimulant-induced reconfigurations of maize metabolism towards growth enhancement and stress alleviation.

The incorporation of biostimulant strategies and programs in the agriculture industry holds promise to sustainably improve crop productivity. Currently, biostimulants, subdivided into microbial and non-microbial categories, are described as formulations that improve plant health and productivity as a resultant action induced by the novel, or emergent properties of the complex mixture, and not only from the presence of a plant growth regulator [ 6 , 7 , 8 ].

The biostimulant market is constantly on the economical rise due to the need to use formulations that promote sustainable soil health, and those that lead to crop improvement with respect to climate resilience and nutrition traits [ 3 ].

Emerging studies have demonstrated the effects of biostimulants on plant physiology and agronomic traits. For instance, the application of a biostimulant on tomato plants showed improved growth and fruit nutritional quality, as well as enhanced antioxidant machineries e. Another study by Paul et al. These changes included alterations in phytohormones and lipids, increases in biomass, stronger stomatal conductance, and enhanced antioxidant defence systems [ 10 ]. Despite ongoing efforts made in studying and understanding the effects of biostimulants on plants, the underlying biostimulant-induced changes at molecular and cellular levels for plant growth promotion and stress resilience remain an active research field.

This knowledge gap hampers the novel formulation of biostimulants and the implementation of these products into agronomic practices [ 3 ]. Hence, in this work, metabolomics was applied to generate fundamental insights regarding the effects of humic substance HS -based biostimulants on maize metabolism under normal and nutrient-starved conditions.

Metabolomics, a multidisciplinary omics science, provides a readout of the metabolome, which carries imprints of environmental and genetic factors. As such, one of the best descriptions of metabolism is the metabolic fluxes it generates, representing the integrated output of the molecular machinery and biochemical characteristics of a biological system [ 11 , 12 ]. Arguably, metabolomics is probably the most challenging and demanding of the omics sciences, due to the inherent complexity of the metabolome.

However, metabolomics-generated insights are increasingly rendered possible as the field positions itself in the current innovative developments in analytical technologies, computational tools and integration of orthogonal biological approaches [ 12 ]. Thus, metabolomics offers unique opportunities in elucidating modes of action of biostimulants, at cellular and molecular levels, necessary insights for the biostimulant industry, and subsequently for a sustainable and improved cropping system.

As mentioned in the Introduction section, this study aimed at elucidating metabolic alterations that explain the effects of a non-microbial biostimulant, a humic substance HS -based formulation, on maize plants under normal and nutrient starvation, in greenhouse conditions. Experimental details are provided in Section 3. For semantic simplicity, the expressions humic substances, HS, humic biostimulant, HS-based biostimulant, and biostimulant will be used interchangeably to refer to the biostimulant formulation used in this study a humic substance-based formulation, Section 3.

Briefly, the study was designed to comprise four 4 different groups, namely, control 1 starved and with no HS , control 2 non-starved and no HS application , and HS-treated under starved and non-starved conditions Table 1 , Section 3. Prior to metabolomic analyses, the morphophysiological assessments were performed to evaluate the effects of the HS-based biostimulant on maize plants under normal and starved conditions.

The HS-treated plants showed increased canopy cover, plant height, above ground dry biomass, improved nutrient uptake and nutrient leaf content under both normal and nutrient starved conditions Figure S1. These morphophysiological traits observed in HS-treated plants can be associated with improved plant health, growth and nutrient stress alleviation.

For metabolomics analyses, metabolites were extracted from leaves and analysed on liquid chromatography—mass spectrometry LC-MS analytical systems, with both untargeted and targeted approaches. Different methodologies and workflows were applied to mine and interpret the generated metabolomics data: these included molecular networking approaches, chemometrics methods, and metabolic pathway and network analyses Section 3.

The spectra data from untargeted analyses were mined using computational tools such as feature-based molecular networking FBMN and MolNetEnhancer housed within the global natural product social GNPS molecular networking environment Section 3. As a result, FBMN allows for spectral annotation, distinguishes isomers, as well as incorporates relative quantification information [ 15 , 16 ].

This method also offers the advantage of giving a more precise estimation of the relative ion intensity by making use of the LC-MS abundance of the features i. The MolNetEnhancer workflow, on the other hand, improves the chemical insight obtained from a dataset by combining outputs from multiple independent computational tools such as molecular networking, MS2LDA MS2 latent Dirichlet allocation , as well as the Network Annotation Propagation NAP in silico annotation tools and thus allowing for enhanced metabolomics data annotation [ 15 ].

In this study, the metabolome covered included unknown classes which had no matches and known or putatively annotated classes, namely, glycerolipids, hydroxycinnamic acid HCA compounds, cinnamic acids and derivatives, carboxylic acids and derivatives, fatty acyls and diazines Figure 1 A , and, as detailed in the methodology experimental, Section 3 , more confirmatory scrutiny was performed to validate the metabolite annotations.

The coloured nodes represent classes of putatively annotated metabolites which were matched to GNPS libraries, whereas the grey nodes represent those unmatched to a library. Clusters of hydroxycinnamic acid HCA compounds B and glycerolipids C with pie charts showing differential changes in metabolite levels under different treatment conditions.

Thus, FBMN and MolNetEnhancer both aided in the putative annotation of some of the metabolites in the extracted maize leaves metabolome. Each node represents a single chemical entity, e. Furthermore, the molecular networking computation also provided a quantitative description of the measured metabolome, pointing to the differential distribution of ions belonging to different classes, as reflected on the pie charts in the clusters of HCA compounds and glycerolipds, showing the effect of HS on the maize plants under normal well-fed and stress starved conditions Figure 1 B,C.

This is further discussed in the subsequent sections. The extracted and annotated maize metabolome comprised different classes of metabolites, as infographically shown in Figure 1 D, suggesting that the metabolic changes in maize plants induced by treatments span a wide spectrum of both primary and secondary metabolic phenomenology.

The application of HS-based biostimulant on maize plants under normal conditions induced coordinated changes in the maize chemical space Figure 1 , significantly impacting pathways for primary and secondary metabolism. Some of these metabolic pathways include alpha-linolenic acid metabolism, amino acid-related pathways such as tryptophan metabolism, glycine, serine and threonine metabolism and cysteine and methionine metabolism , and secondary metabolism pathways such as phenylpropanoid pathway and flavonoid metabolism Figure 2 A; Table S1.

Maize plants treated with the humic biostimulant showed increased levels of oxylipins such as oxo-phytodienoic acid OPDA , hydroperoxy-octadecatrienoic acid HpOTrE 1, hydroperoxy-octadecatrienoic acid HpOTrE 2 and oxo- pentenyl cyclopentaneoctanoic acid OPC , components of alpha-linolenic acid metabolism Figure 2 B.

Although the mechanistic roles of these individual oxylipins are still poorly understood, some of the general functions of oxylipins in plants include modifications of chloroplast function, plant senescence, stomatal conductance, and antifungal and antibacterial activities [ 18 ].

Furthermore, the oxylipin pathway leads to the generation of the phytohormone, jasmonic acid. Moreover, other signalling metabolites such as indole acetic acid IAA and salicylic acid SA were found increased in maize plants treated with HS compared to the control Figure 2 C. These phytohormones are regulatorily involved in various biochemical and physiological processes in plants, such as seed germination, seedling growth, stomatal aperture, respiration, and in interactions with the environment [ 19 , 20 ].

Thus, the measured changes in lipids and hormonal signalling networks in maize plants Figure 2 B,C suggest that the HS biostimulants remodel maize metabolism towards growth promotion via the activation and enhancement of physiological events for improved plant development and the potentiation of defences [ 21 , 22 , 23 ].

A summary of metabolic pathway analysis generated using MetPA, pathway mapping and relative quantification of some altered amino acid, hormones, oxylipins and phenolic compounds. B Linoleic metabolism. C Absolute quantification of selected hormones. D Serine biosynthesis. E Cysteine and methionine metabolism. F Carbon fixation in photosynthetic organisms.

G Secondary metabolism, relative quantification of selected phenolics. Other abbreviations are found in Table S2. Furthermore, other metabolic remodelling induced by the HS treatment on maize plants under normal conditions included a general increase in the levels of amino acids Figure 2 E,F.

Amino acids play indispensable roles in metabolic pathways governing the plant growth and development processes. Plants do not only harvest atmospheric carbon dioxide for the production of photosynthates, they also utilize the internal carbon pool [ 24 , 25 ]. Thus, it can be postulated that increases in Ala and Asp levels contribute to an increased pool of the internal carbon, which could be used in photosynthetic reactions, thus supporting growth promotion.

Correspondingly, the study of Vaccaro et al. Met is also involved in a wide range of functions in plant growth and development; for example, it provides a required supply of sulphur and nitrogen to plants [ 28 ]. Thus, in this study, it can be postulated that the HS-induced increased level of Met was also translated into the measured increase in sulphur and nitrogen contents Figure S1A , a growth promotion mechanism.

Moreover, Met is also known to maintain the structure of proteins required for cell differentiation and division [ 28 ]. Other changes in amino acid levels included an increase in Ser levels in HS-treated maize plants, under normal conditions Figure 2 E. Ser is synthesized through three routes: i the glycolate pathway photorespiration ; ii glycerate pathway cytosolic glycolysis ; and iii phosphorylated pathway Calvin cycle Figure 2 D.

Thus, our results suggest that the application of HS may have impacted these pathways, leading to the accumulation of Ser Figure 2 E. Apart from its proteinogenic roles, Ser takes part in the biosynthesis of several biomolecules required for cell proliferation, including amino acids, nitrogenous bases, phospholipids, and sphingolipids. Furthermore, it also plays an indispensable role in signalling mechanisms, as one of the three amino acids that are phosphorylated by kinases [ 29 ].

Ser is also involved in another significantly impacted pathway: Gly, Ser and Thr metabolism Figure 2 A; Table S1 , which plays an important role in plant photorespiration [ 26 ]. The accumulation of amino acids in HS-treated plants Figure 2 E,F and Figure S2A also suggests an increased pool of substrates for protein synthesis, which is positively associated with increased plant biomass [ 30 ]. Agreeably, these metabolic measurements were translated into the maize phenotype, because HS-treated plants showed higher plant biomass, an HS-enhanced growth and development Figure S1C.

The application of the HS-based biostimulant on maize plants under normal conditions also impacted the secondary metabolism, as revealed by molecular networking approaches Figure 1 and metabolic pathway analysis Figure 2 A,G and Table S1. In this study, under normal conditions, most flavonoids such as quercetin, luteolin neohesperidoside, kaempferol and isorhamnetin rutinoside levels were decreased in plants treated with HS compared to non-treated plants Figure 2 G.

Moreover, the application of HS showed a differential response of HCA compounds, namely, chlorogenic acids and cinnamoyl hydroxycitric acid esters Figure 2 G. Primary and secondary metabolisms are involved in the use of the available photosynthetic assimilates, leading to trade-offs of the carbon allocation. In nutrient-rich environments, large amounts of carbohydrates are allocated to primary metabolism protein synthesis , while secondary metabolism phenolics production is limited [ 31 , 32 ].

The latter could be a possible reason for the observed reduction in flavonoid contents in HS-treated plants compared to control plants, under normal conditions. Furthermore, the decreased levels of some HCA compounds 3- and 5-caffeoylquinic acid, caffeoyl hydroxycitric acids and caffeoylglutarate; Figure 2 G may suggest that the phenylpropanoid pathway was not favoured in HS-treated plants under normal physiological conditions, regardless of the accumulation of the precursors of this pathway, of Phe and Tyr in HS-treated plants vs.

This result further supports the above-mentioned hypothesis that the carbon from these amino acids is mainly directed towards the primary metabolism, thereby prioritizing plant growth. With the phenylpropanoid pathway not being stimulated, this may have affected the downstream pathways such as flavonoid metabolism; thus, a general decrease in flavonoids levels in HS-treated plants Figure 2 G.

However, some phenolic compounds such as tricin diglucuronide, 3-feruloylquinic acid, coumaroylquinic acid and coumaroyl hydroxycitric acid were increased under HS treatment Figure 2 G. This points to a dynamic and complex network of phenolic compounds, reconfigured by biostimulant treatment for the enhancement of growth and development of maize plants, under normal conditions [ 33 ]. These HS biostimulant-induced metabolic alterations accumulation of lipids, hormones and amino acids and differentially changed phenolic compounds under normal conditions Figure 2 were synchronously translated into agronomic traits: the maize plants treated with the humic substances showed increased canopy cover, plant height, plant diameter, above ground dry biomass and chlorophyll content Figure S1C , and enhanced plant growth mediated by HS-based biostimulant application.

The HS-biostimulant-induced global metabolic reprogramming under nutrient starvation spanned a wide range of metabolic classes such as flavonoids, HCA compounds, lipids, amino acids and hormones Figure 1. Enhanced amino acid degradation is usually observed in plants suffering from C deficiency [ 34 ].

However, the application of HS to starved plants showed an increase in these amino acids compared to non-treated starved plants Figure 3 A. This could mean that HS either directly supplies the plants with C and N or it triggers other mechanisms which efficiently provide the plant with sufficient C and N. Several studies have shown that the application of HS enhances the acquisition and mobilization of nutrients such as N amongst others. N is known as the most essential nutrient in plants, because its metabolism is the basis of biological molecules such as amino acids, proteins, nucleotides and enzyme synthesis [ 35 , 36 , 37 ].

The increase in amino acids observed in starved HS-treated plants compared to the untreated starved plants Figure 3 A can thus be correlated with the increased absorption of N Figure S1. Relative quantification and pathway mapping of annotated metabolites under nutrient starvation. A Heatmaps and bar graphs showing the relative and absolute quantification of amino acids, HCA derivatives and flavonoids.

B Pathway mapping of annotated oxylipins and their differential distribution in control, HS-treated and untreated under nutrient starvation. With regard to the HCA compounds and flavonoids, metabolites which were increased in non-treated starved plants e. In contrast, phenolic compounds that were decreased in non-treated starved plants e.

Oxylipins have been shown to be involved in stress signal transduction, the regulation of stress-related gene expression, and interaction with hormonal signalling pathways [ 38 ]. The growth and stress hormones, IAA and ABA abscisic acid , respectively, were decreased by the application of HS under nutrient starvation Figure 3 C , suggesting homeostasis towards normal condition.

Generally, under abiotic stress conditions, plants biosynthesize higher levels of ABA, which induce stomatal closure and inhibit the growth and development of plants [ 39 , 40 ]. The level of IAA was increased under nutrient starvation in non-treated maize plants Figure 3 C , which correlated with previous studies [ 41 ].

Overall, these metabolic alterations suggest that the application of HS under nutrient starvation induces metabolic readjustments to alleviate the negative effect of starvation in plants. It can then be postulated that HS-based biostimulant treatment led to a rewiring of the maize metabolism for the efficient acquisition and use of resources under limited supplies of nutrients.

This HS-induced metabolic remodelling towards stress alleviation correlates to the observed in-plant nutrient profiles; the uptake of macronutrients such as K, N, Ca, Mg, P and S and micronutrients such as Na, Fe, Zn, Mn, B and Cu was higher in starved plants that were treated with HS biostimulant compared to non-treated plants Figure S1A.

Moreover, the nutrient leaf analysis showed that the leaves of HS-treated starved plants contained higher levels of nutrients compared to non-treated starved plants Figure S1B. Furthermore, these metabolic changes and nutrient profiles were translated into phenotypically observable agronomic traits such as improved plant height, above ground dry biomass, and canopy cover Figure S1C.

To distinctively map and globally visualize the metabolomic data, a metabolic network analysis was performed using MetaMapp. This web-based tool is able to map all detected metabolites into network graphs using the KEGG reactant pair krp database and Tanimoto chemical similarity between PubChem substructure fingerprints, thus generating an overview of the metabolic regulation under specified conditions [ 42 ].

The chemical similarity feature was implemented on the foundation that biochemistry is described as the inter-conversion of chemically similar entities. This information can thus assist in the prediction of the enzymatic transformation networks between the biochemical domains [ 43 ]. As infographically depicted on the metabolic networks Figure 4 , there are three main metabolic clusters, namely, phenolics indicated by the circles , lipids arrows , and amino acids squares , which are mainly interconnected based on their chemical similarity grey edges.

Hormones diamonds such as indoles e. Thus, the correlation network computed comprised structural similarity complemented by krp interactions to avoid the misclustering of some obviously biologically related compounds and to reveal the biochemical reaction networks [ 44 , 45 ]. The biochemical reaction network amongst the amino acids highlights Ala as a metabolite hub of the network, with many krp edges connecting to the Ala node Figure 4.

This point to the tight regulation of the amino acid metabolism and may warrant a closer look into the potential roles of Ala as a regulator. Ala metabolism has been shown to be tightly linked to carbon and nitrogen metabolism, the TCA cycle and sugar metabolism [ 46 ]. MetaMapp metabolite network visualization depicting the effects of nutrient starvation on A non-treated plants and B HS-treated plants. Red nodes indicate increased metabolites, whereas the blue indicates a decrease.

Node size indicates the magnitude of fold-change. Compounds are connected by KEGG reaction pair krp, green line , and chemical similarity grey line. Furthermore, MetaMapp analysis utilizes statistical information such as the p -value and fold-changes [ 43 ]. Thus, the generated metabolic networks revealed significantly altered metabolites in HS-treated Figure 4 B and non-treated Figure 4 A plants under nutrient starvation illustrated by node attributes such as size and colour.

Ala was decreased in non-treated plants in response to nutrient starvation, and the other amino acids which are connected to Ala were also decreased Figure 4 A. However, in HS-treated starved plants, Ala was increased while its interconnections were either increased or unchanged Figure 4 B. Moreover, a study by Ishihara et al. This further supports the functional role of Ala as a potent regulator of amino acid metabolism.

Furthermore, the application of these metabolic network maps allowed for the detection of metabolites which were significantly altered by HS application under starvation. For instance, observing the phenolics cluster, compounds such as kaempferol rutinoside, rutin, luteolin rutinoside, and caffeoylglutarate were significantly changed compared to other compounds which showed no significant changes Figure 4 B; see Table S3 for p -values.

The computed metabolic network Figure 4 points to possible regulatory events underlying the HS-induced metabolic reconfiguration in maize plants towards growth enhancement and the alleviation of nutrient starvation. Thus, a mechanistic model emerging from the present study provides key fundamental insights describing a hypothetical metabolic framework underlying the effects of HS-based biostimulants on maize plants, under normal and nutrient-starved conditions Figure 5.

Metabolic reconfigurations related to the HS biostimulant-induced growth promotion involves differential alterations in the levels of amino acids, phenolics and lipids, which are translated into physiological events such as i membrane remodelling, ii improved chlorophyll content and photosynthesis rates, iii improved N and C assimilation, iv elongation of roots and shoots, and v increased nutrient uptake and assimilation Figure 5.

A contextual summary of postulated mechanisms elucidated from this study. The left side of the plant highlights the changes in metabolites involved in the key impacted pathways, leading to growth-promoting physiological events under HS treatment in non-starved conditions. On the right side of the plants are the biochemical alterations in the levels of metabolites spanning the impacted pathways identified in the HS-treated, starved plants, which were associated with the HS-enhanced alleviation of nutrient starvation.

The study was experimentally designed to comprise different treatments or groups Table 1 , i. Each pot was considered as a biological replicate and contained five plants at the harvesting time. Five biological replicates i. The detailed descriptions and preparation of this HS-based formulation are not disclosed, because these biostimulant products are Omnia trade-marked and still undergoing commercialization processes. Harvesting of the plant materials was performed 3-days after the application of humic substances.

The leaves were harvested and immediately shock-frozen in liquid nitrogen to quench all metabolic reactions [ 47 , 48 ]. For metabolite extraction, the harvested leaf samples were crushed to a fine powder using liquid nitrogen in a mortar. The quality controls QCs , consisting of pooled equivalent volumes from the control and treatment groups, were prepared. An ultra-high-performance liquid chromatography UHPLC system coupled to a high-definition quadrupole time-of-flight MS instrument Waters Corporation, Manchester, UK was used to analyse the aqueous-methanol extracts, for the nontargeted approach.

The binary solvent system comprised solvents A 0. The analytical column was allowed to calibrate for 2 min before the next injection. The total chromatographic run time was 20 min. The chromatographic effluent was further analysed as follows: a SYNAPT G1 high-definition mass spectrometer, equipped with electrospray ionization ESI source, was used for untargeted analysis. The MS detector was set to acquire centroid data in both positive and negative ionisation modes. Analysis of each sample was performed in triplicates.

For downstream structural elucidation, the MS analyses were set to result in both unfragmented and fragmented experiments through collision-induced dissociation MS E , where the fragmentation patterns were obtained by alternating the collision energy from 10 to 50 eV. A multiple reaction monitoring MRM method was used for absolute quantification of the targeted metabolites amino acid and hormones Table S4 : descriptions of the LC and MS parameters are detailed in Nephali et al.

The following parameters were used for data processing: retention time Rt range of 1—17 min, a — Da mass range, intensity threshold of 50, mass tolerance of 0. Normalization was then performed by using total ion intensities of each defined peak; prior to calculating intensities, the software performs patented modified Savitzky—Golay smoothing and integration. Some of the computed chemometrics models were included principal component analysis PCA. The latter is an unsupervised method that aims at data dimensionality reduction and generates a model that reveals clusters, trends, and similarities between treatment groups [ 12 ].

Supervised, orthogonal partial least squares-discriminant analysis OPLS-DA models were also computed for binary sample classification and generating the descriptive statistics. MetaboAnalyst version 5. Before building the chemometrics models e. A nonlinear iterative partial least squares algorithm in-built within SIMCA software was used to handle the missing values, with a correction factor of 3.

A sevenfold cross-validation CV method was applied as a tuning procedure in generating the models, and only the components positively contributing to the prediction ability of the model R1 significant components were considered. Thus, to ensure reliable results, only thoroughly validated and preferably parsimonious models were considered in this study. Quantitative analysis i. All the raw vendor i. The MS-DIAL data-processing program makes use of a deconvolution algorithm to perform mass spectral deconvolution of data-independent acquisition DIA data, thus making it applicable for the extensive untargeted metabolomics analysis of both DIA and data-dependent acquisition DDA centroid datasets [ 53 ].

The data were processed using the following parameters: mass accuracy MS1 and MS2 tolerance of 0. A feature-based molecular network FBMN was generated for both the negative and positive mode data by uploading the respective feature quantification table, MGF file and a metadata file describing the properties of the sample file i. This approach builds on the assumption that molecules which are structurally related give rise to similar fragmentation patterns when subjected to MS 2 fragmentation, for example, collision-induced dissociation CID , thus allowing for molecular networks to be created [ 14 , 54 ].

The MolNetEnhancer networks, on the other hand, were coloured based on the classes such that nodes present in the same class had the same colour while grey nodes represented the non-matched metabolites. The fragmentation spectra of all the putatively annotated metabolites matched to the GNPS spectral libraries were manually validated using the metabolite annotation workflow described below. In the current study, metabolites were putatively annotated to level 2 of the Metabolomics Standards Initiative MSI [ 55 ].

All annotated and targeted metabolites Tables S2 and S4 were used for metabolic pathway and network analyses. Metabolic pathway analysis was performed with the Metabolomics Pathway Analysis MetPA component of the MetaboAnalyst bioinformatics tool suite version 5.

This enabled the identification of the affected metabolic pathways, analysis thereof, and visualization. In addition to the existing literature, the use of these bioinformatics tools for pathway analysis provided a framework to partially map the molecular landscape of the metabolism under study, enabling the biological interpretability of observed changes in a metabolome view [ 44 ]. A Tanimoto score threshold of 0. The generated networks were visualized using Cytoscape v3.

Understanding the modes of action involved in biostimulant-mediated growth promotion and stress resilience is one of the critical steps necessary for the full implementation and integration of biostimulants into agricultural practices. Thus, this present study intended to decode a metabolic choreography that defines the effects of an HS-based biostimulant on maize plants, under normal and starved conditions, in a greenhouse setting.

Although further investigation may be needed to build on our findings, the model derived from this metabolomics study suggests that the HS-biostimulant induced a metabolic reprogramming in maize plants towards growth promotion and the alleviation of starvation stress. Molecular networking approaches aided in characterizing the HS-altered chemical space. In more detail, a wide and coordinated range of metabolic processes was involved in the response of maize plants to HS treatments.

Impacted metabolic pathways included amino acid metabolism, phenylalanine metabolism, and alpha-linolenic acid metabolism, among others, involving a spectrum of metabolite classes such as amino acids, phytohormones, lipids, HCA compounds and flavonoids which are involved in growth promotion and nutrient stress alleviation.

Furthermore, metabolic network analysis revealed some qualitative characteristics of HS effects on maize metabolism under nutrient starvation: a complex structural interconnectivity between altered metabolites involved in stress alleviation and metabolite hubs depicting possible biochemical regulatory mechanisms, which can be investigated further. I suspect if there are not keys don't ahve any servers like that here , then ssh credentials will be requested. Ok, Thanks going to test all this later on.

Will post when I get it all working. Don't know why filezilla shouldn't work the same unless the SFTP environment is chroot'd. Ok got it all working. I removed proftpd and installed vsftpd configure it to sftp. It gets carefully reviewed, since it is the cornerstone for most UNIX administrators. It has amazing security features, is well understood, and documented.

Why not use it? I need some type of s ftp access to my server. I host a a few websites for friends. Vsftpd and Proftpd are the most used ftp servers. I dont know any other way to set this up. Clearly, it is up to you. There is no need for any FTP servers. Use the built-in sftp server that is already running as part of the openssh-server on the box. You can limit where certain users have access and only allow sftp, preventing scp, ssh use at the userid level.

Hmm, thats instresting. You meen like that. Dident know that at all. Did not know you could run ssh whit sftp. Just thought you had it to putty client. Going to test ofcourse : And it works whit standard ftp clients like filezilla and winscp? The ability for a client program to reject the security is troubling. Clients shouldn't get to choose what the security level used is, IMHO, especially if passwords are being transmitted.

Ok got it all working but used a lite easier way to lock user in home dir. Now then, this must be ok or?

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Learn more. Asked 9 years, 1 month ago. Modified 7 years, 6 months ago. Viewed 23k times. Improve this question. MeltingDog MeltingDog 1, 3 3 gold badges 19 19 silver badges 32 32 bronze badges. Do you have SSH access? Are you on a Windows server? Add a comment. Sorted by: Reset to default. Highest score default Date modified newest first Date created oldest first. Improve this answer. The control Panel in Plesk I assume is akin to the Security Tab of the Properties Window in Windows, which is why you are able to "via the control panel" — eyoung Josh Mountain Josh Mountain 4 4 silver badges 15 15 bronze badges.

Sign up or log in Sign up using Google. Sign up using Facebook. Sign up using Email and Password. Post as a guest Name. Email Required, but never shown. The Overflow Blog. Time to get on trend. But if the same script is changed to work as Requesting somebody to help me understand what is going wrong here.

Join our community to see this answer! Unlock 1 Answer and 4 Comments. Andrew Hancock - VMware vExpert. See if this solution works for you by signing up for a 7 day free trial. What do I get with a subscription? With your subscription - you'll gain access to our exclusive IT community of thousands of IT pros. We can't always guarantee that the perfect solution to your specific problem will be waiting for you. If you ask your own question - our Certified Experts will team up with you to help you get the answers you need.

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